We Evaluate AI Systems In Production
Your AI doesn’t run in a lab. Neither should your evaluations.
AI isn’t traditional software
it’s dynamic, complex, and unpredictable, running in multi-tiered supply chains. Its code carries promise but also risks and liability. Rogue AI can result in performance errors, inefficiencies, reputational risk, financial losses, and compliance gaps.
We help you build the AI risk management evidence that leadership and regulators actually require.
We give you back control.
Independent evaluations of AI systems since 2012
Clients across 4 continents
Benchmarks from 15+ industries. 200+ systems evaluated
Whatever your system, we evaluate it end-to-end in its real environment
Expert & predictive systems
Classifiers, scoring models, and decision-support tools. We evaluate data quality, threshold logic, explainability, bias across populations, and whether human review actually changes outcomes.
LLM-based systems
Generative AI, RAG pipelines, and AI-assisted workflows. We evaluate hallucination rates, output explainability , retrieval quality, prompt safety, output consistency, and data leakage risk.
Agentic systems
Autonomous agents, multi-step pipelines, and tool-using models. We evaluate action traceability, goal alignment, failure modes, and whether human oversight is adequate for the level of autonomy granted.
Real clients. All verticals. Real impact.
Independent
Why Eticas
With no interest in the outcome, we objectively quantify any risk.
Socio-technical
We evaluate the full system: data, model, business rules, software, people and process.
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Proprietary methodology and tech built over more than a decade of auditing systems in production.
Guided by Experts
Evaluating AI since 2012. We don’t stop at metrics. We give you the evidence and guidance to act.
We deliver where others stop
Governance Platforms: Track policies & checklists
Observability Tools: Track technical metrics
Audit Firms: Deliver one-off reports
Eticas: Continuous, independent, socio-technical evaluation in production
Outcomes of good AI assurance vs poor practice
With Independent Evaluation:
Safe, trustworthy AI adoption
Client and user confidence
Risk Mitigation and explainability
Successful AI Integration
Access to Insurance
Without Independent Evaluation
Hidden harms surfacing in the wild
Costly remediation and wasted effort
Reputation risk
Legal liability